The present invention relates to 3D computed tomography (CT) colonography, and more particularly, to automated polyp segmentation for 3D CT colonography.
Colon cancer is the number two cause of cancer death in men and women combined, but it is one of the most preventable cancers because doctors can identity and remove pre-cancerous growths known as polyps. 3D CT colonography (CTC), or virtual colonoscopy, is emerging as a powerful polyp screening tool because of its non-invasiveness, low cost, and high sensitivity. 3D CTC visualizes the colon, and allows a physician to navigate the colon to search for polyps. However, the physician needs to manually adjust the navigation speed and change the angle in order to see a polyp clearly. For example, a polyp may be hidden in a colonic fold and thus could be missed during the physician's visual inspection. Accordingly, there has been much research in computer aided detection (CAD) of polyps in CTC, and several such CAD systems have been proposed. Once a polyp is detected either manually or automatically, the polyp is measured and classified by a physician. However, there is a large variability in physician's measurements of polyps. Therefore, an accurate, consistent, and automated method for polyp measurement is desirable.
Polyp segmentation is defined as extracting as isolating a polyp from the colon wall at a given location. In addition to its significant value for polyp measurement in clinical practice, polyp segmentation is also important for computer aided detection of polyps. Polyp segmentation is a challenging task because polyps are abnormal growths from the colon wall and the “expected” segmentations are often a semantic, perceptual boundary with low imaging contrast support. Furthermore, there are multiple shape categories of polyps, such as sessile, pedunculated, flat, etc., with a large 3D shape/appearance variation. Conventional polyp segmentation methods utilize unsupervised segmentation or clustering in 3D data volumes. Such methods include, fuzzy clustering, deformable model or snakes, variational level-set method, and heuristic surface curvature constraints. These unsupervised approaches work well for up to 70% of polyps due to unclear polyp/nonpolyp boundaries, large within-class polyp appearance/shape variations, or limited heuristic shape assumptions. Accordingly, a polyp segmentation method having increased accuracy is desirable.